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新论文 · v1arXiv:2611.02637

Variable names provide priors: semantic grounding enables data-efficient numerical prediction by large language models

Xin-Rui Ma, Shi-Zhi Chen, Hai-Yan Jiang, De-Cheng Feng, Michael Beer

原文摘要

Large language models (LLMs) have achieved strong performance across diverse tasks, from question answering to code generation. Recent studies have found that LLMs can perform numerical prediction on tabular datasets via in-context learning. However, it remains unclear under what conditions and how LLMs use variable names and in-context examples for numerical prediction. Here we show that semantic grounding, the match between variable names and the values they describe, allows five LLMs without task-specific training to match or outperform seven supervised machine learning (ML) models across 20 datasets from nine scientific domains, particularly when examples are scarce. Reasoning traces indicate that variable names allow LLMs to draw on domain-specific knowledge acquired during pretraining. Using controlled tasks based on physical formulas, we further show that LLMs integrate domain-specific knowledge with in-context examples in a way consistent with Bayesian updating. Specifically, when the relationship between the inputs and the target cannot be inferred from the examples alone, LLMs rely on domain-specific knowledge as the prior. Conversely, as contradicting examples accumulate, LLM predictions shift progressively towards these examples, which act as the likelihood. Our work reveals how LLMs use domain-specific knowledge in numerical prediction and positions them as data-efficient predictors for data-scarce scientific domains.

cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02634

Evolving Explanations: Complexity-Adaptive Refinement of Faithful Explanations for Symbolic Regression

Elisabeth Mayrhuber, Ting Hu, Stephan Winkler

原文摘要

Natural-language explanations generated by large language models (LLMs) for machine learning models often seem fluent but are not necessarily faithful to the underlying model. We formalize this mismatch as a faithfulness gap and propose a complexity-adaptive refinement framework for symbolic regression that iteratively generates and validates explanations until numerical agreement with the target expression is achieved. The approach follows a two-stage pipeline: an explainer LLM produces textual descriptions at increasing levels of detail, while an independent validator LLM extracts a symbolic expression that is compared against the original model. This process is framed as a discrete search over explanation space, where complexity is minimized subject to a faithfulness constraint. We evaluate the method on real-world and synthetic datasets, with a total of 250 runs across varying model complexities. The explanation refinement loop achieves an overall pass rate of 84.0%, with most explanations converging at low complexity levels and only a small fraction requiring detailed descriptions. Our results demonstrate that faithful explanations can be obtained with minimal necessary complexity by explicitly closing the explanation loop through symbolic validation.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02633

Measuring AI Agents as Teammates with Collaborative Intelligence

Sean Wu, Aneesh Pappu, Jiaru Zou, Yubin Kim, Yuncong Mao, Hongjian Zhou, Junchi Yu, Jieneng Chen 等 9 位作者

原文摘要

Progress in AI is often measured by what a single model can do on its own. Yet teams of AI agents are starting to make breakthroughs and discoveries in science, medicine, mathematics and software engineering. As AI begins to work in teams of agents and people, the ability to collaborate becomes a core part of their intelligence. Research on human teams has measured this ability for decades. Inspired by this, we introduce the Collaborative Intelligence Benchmark, which measures how well an AI agent works as a teammate. It contains 240 tasks across 8 dimensions of teamwork, from refusing an unsafe shortcut to checking the work that a weaker model leaves behind. Each task comes from a real record or an existing benchmark, from aviation accident reports and clinical cases to coding tasks and negotiations, and has a programmatically verifiable outcome. To isolate teamwork from individual capability, models are scored only on tasks that they can solve by themselves when they have full information. We evaluate 18 closed- and open-weight models and find that every model, across sizes and families, has clear weaknesses as a teammate, with the best model scoring only 82.2 out of 100. Frontier models almost always refuse unsafe shortcuts and report misconduct, yet they cave under interpersonal pressure, reaching the best overall deal in only 14% of conflicts, and their pass rate drops from 97% to 81% when they take over a task in which a weaker model has cheated. Crucially, general capability does not guarantee good teamwork, as models with nearly the same capability score differ by up to 11 points as teammates. The benchmark includes 160 public tasks with code and data, along with 80 held-out tasks for evaluation. As AI agents increasingly work alongside other agents and people, the ability to collaborate effectively is becoming a crucial capability to measure and develop.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02632

Susceptibilities for Neural Networks Learning from Physical Data

Rohan Hitchcock, Gary W. Delaney, Jonathan H. Manton, Richard Scalzo, Jingge Zhu

原文摘要

Neural networks that are trained on data arising from a physical system must somehow learn regularities induced by the underlying physical laws. In this setting, concepts from statistical physics provide powerful tools for analysing how physical laws influence the learning process. In this paper we explain how susceptibilities --- which measure the response of a neural network to perturbations of a parameter of the data distribution --- can be applied when learning from physical data. We show that such susceptibilities can identify the conditions under which specific input features are most informative for learning, guiding data selection, a prediction we validate experimentally. For distributions lacking a natural parameter, we propose introducing one via a physically meaningful scalar function of the physical state, and demonstrate that susceptibilities track the emergence of specific capabilities (such as collision detection) during training.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02631

UniCross3D: Unified Cross-View and Cross-Domain Diffusion for Consistent Single-Image 3D Generation

U-Chae Jun, Jaeeun Ko, Jiwoo Kang

原文摘要

Reconstructing detailed geometry and realistic appearance from a single RGB image is essential yet fundamentally challenging due to inherent ambiguities such as occlusion, lighting variations, and texture-geometry entanglement. While recent diffusion-based generative models have significantly improved novel view synthesis, existing approaches suffer from two critical limitations: lack of cross-view geometric consistency and insufficient cross-domain semantic alignment. To address these issues, we introduce UniCross3D, a unified cross-view and cross-domain diffusion framework designed explicitly for consistent and physically coherent 3D generation. UniCross3D features two novel contributions: (1) a cross-view latent regularization that enforces cross-view geometric consistency across synthesized viewpoints by penalizing latent variance, and (2) a cross-domain mutual information objective grounded in the physics of image formation, explicitly aligning synthesized color and normal maps. Extensive experiments demonstrate that UniCross3D achieves significantly improved view consistency and semantic alignment over state-of-the-art methods and yields higher-fidelity reconstructions, particularly under challenging textures and ambiguous viewpoints.

cs.CV · cs.GR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02630

ICJ-Graph: A Citation Graph and a Layered Document Corpus of the International Court of Justice

Jirameth Wannasiwaporn, Pawitsapak Akarajaradwong, Chompakorn Chaksangchaichot

原文摘要

We present ICJ-Graph, a machine-readable resource connecting documents of the International Court of Justice (ICJ) to cited cases, legal provisions and United Nations resolutions. Court decisions, written pleadings and oral records form distinct document layers, preserving the distinction between the Court's reasoning and the parties' submissions. The collection contains 6,396 ICJ documents and 505 documents from other courts and legal bodies, together with 12,375 provisions and 20,291 UN resolutions. The data files contain text, metadata and embeddings. The accompanying pipeline lets users load the graph and generate embeddings for use in their own retrieval systems. To illustrate the resource's use, we evaluate five models on citation prediction for 199 decisions using documents available before each decision. Tools designed for citation research improve citation-prediction F1 over answering without tools for all five models across case, provision and resolution citations.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.17450

VLM2Rec: Resolving Modality Collapse in Vision-Language Models for Sequential Recommendation

Junyoung Kim, Woojoo Kim, Jaehyung Lim, Dongha Kim, Wonbin Kweon, Hwanjo Yu

原文摘要

Sequential Recommendation (SR) learns sequence-level Collaborative Filtering (CF) from interactions, but ID-based models struggle with sparse data and cold-start items. In contrast, item titles and images can reveal preference continuity, such as a recurring color or style. Yet existing approaches miss visual continuity: small encoders take a single item as input, and Large Language Models (LLMs) read only titles. We therefore shift our focus to Large Vision-Language Models (VLMs), which can read a user's entire image sequence, not just its titles. However, VLMs inherently suffer from modality collapse: they over-rely on text and, as a result, overlook visual continuity for recommendation. We find that standard contrastive Supervised Fine-Tuning (SFT), essential for adapting VLMs to SR, further amplifies this collapse: updates follow the text gradient, visual discrimination weakens, and adding image embeddings to the text embeddings consistently lowers accuracy. We propose VLM2Rec with two objectives: Weak-modality Penalized Contrastive Learning strengthens each user's weaker modality, and Cross-modal Relational Topology Regularization aligns each user's relative similarity structure over the same items across the two modality spaces. Its variant, $\text{VLM2Rec}_{\text{plug}}$, reuses the learned item embeddings in SR backbones and thus serves at the latency of conventional SR models. Experiments show that VLM2Rec mitigates the collapse and consistently improves accuracy and robustness.

cs.IR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.11308

2AM: Grounding Agent-Side Memory as Guidance for Steerable Action Models in Long-Horizon Manipulation

Yutong Hu, Fengjiao Chen, Xuezhi Cao, Renaud Detry

原文摘要

Long-horizon robot manipulation requires memory, but not necessarily inside the action policy. To address such tasks, current agentic systems often combine VLAs with planners and geometric tools, sometimes using additional depth or calibrated geometry. These systems confound attribution: gains may come from richer observations or alternative motor tools, while failures may stem from either the policy or an under-specified language interface. We isolate this question through a deliberately constrained design: less tool breadth, but greater interface bandwidth. 2AM makes a multimodal Agent the sole holder of task memory and a single RGB-based, episodically stateless Action Model the sole executor of task-relevant motion. The Agent compiles interaction history into subtask language and optional 2D grasp, place, and move hints that bind its physical intention at different time scales. To teach this steerability to the VLA, we augment demonstrations with structured hint labels and train under condition dropout, spatial noise, and temporal jitter to tolerate imperfect Agent outputs. On LIBERO-Mem, without depth, online geometry, or planner-based object motion, 2AM reaches 76.3% average completion, a 61.5-point improvement over the strongest reported baseline of 14.8%, together with 63.0% relaxed and 11.8% strict success. These results show that task memory can remain Agent-side. They further show that Action Model capability depends not only on what the policy has learned, but on how precisely the Agent can steer it. Project website: agent-mem-action-model.github.io

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02628

ReplayFactor: Replay-Aware Dual-Attribute Learning for Practical Speech Deepfake Detection

Miao He, Jiayi Zhou, Yu Zheng, Zhongjie Ba, Peng Cheng, Yuxi Zhou, Junjie Zhu, Changhao Zhang 等 9 位作者

原文摘要

Advances in synthesis techniques have made deepfake speech increasingly realistic, amplifying voice security threats, including unauthorized voice cloning and impersonation. Although existing deepfake detectors perform well on digitally generated speech, their performance often degrades on physically replayed deepfakes, which constitute a low-cost and practical threat to real-world systems. Existing research focuses primarily on dataset construction and evaluation, while available dataset diversity and replay-aware detector design remain limited. We propose ReplayFactor, a dual-attribute framework that enhances replay evidence by fusing self-supervised learning (SSL) representations with an acoustic prior, while leveraging multi-task learning and supervised contrastive learning to promote robust, generalizable representations. We also construct ReplayFake-CN, a large-scale, multi-device Chinese dataset comprising approximately 185k replayed synthetic utterances to complement public benchmarks. Our framework improves macro-F1 from 60.99% to 76.44% and reduces EER from 11.86% to 3.05% on the EchoFake open set over the baseline, demonstrating strong potential for practical replayed deepfake detection.

cs.SD · eess.AS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02627

Recovering Missing Degrees of Freedom in Simulation-Trained Field-Level Generative Models

Masato Shirasaki, Shiro Ikeda

原文摘要

Simulation-trained generative models are emerging as a powerful framework for field-level inference in cosmology, particularly in strongly nonlinear regimes where analytical approaches become intractable. However, the finite size of training datasets often leads to variance shrinkage, indicating that part of the realization-level variability of the underlying physical process is not captured by the learned generative dynamics. In this Letter, we interpret variance shrinkage as a diagnostic of missing effective degrees of freedom and introduce physics-informed latent modifications to the generative dynamics of a continuous normalizing flow. We show that these latent corrections recover a substantial fraction of the missing variability without requiring additional training simulations. Using weak-lensing field-level inference as a representative cosmological application, we further demonstrate through posterior analysis that the learned latent directions are actively exploited during inference and behave as effective nuisance parameters that absorb part of the mismatch between the training and target distributions. As a result, the model exhibits improved robustness to out-of-distribution shifts in field-level inference. Our work establishes variance shrinkage as a practical diagnostic for identifying missing degrees of freedom in simulation-trained generative models and provides a framework for recovering them through physics-informed modifications of generative dynamics.

astro-ph.CO · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02626

Neural Integral Control Barrier Functions for Agile Collision Avoidance

Dasari Raj Vamsi, Vrushabh Zinage, Efstathios Bakolas, Rohan Chandra

原文摘要

Control barrier functions (CBFs) are a standard way to keep a planner inside a safe set, but they assume control affine dynamics, a barrier function given in closed form, and a known nominal controller. Integral control barrier functions (ICBFs) remove the first assumption and absorb input constraints, yet they still assume a given nominal controller and a barrier function. In this work, we present an approach to learn all three from data, using neural ordinary differential equations (ODEs) for the dynamics, imitation for the nominal controller, and supervised regression for the barrier function. The learned dynamics, nominal controller, and the barrier function are used to build the ICBF filter constraint analytically. Furthermore, the barrier is trained on viability rather than geometry, meaning that a state counts as safe only if some emergency maneuver can still avoid the constraint under the actuator limits, a correction that geometric labels miss entirely and that grows with speed. Actuator limits then enter the online program as hard bounds while the learned barrier rows may be relaxed, which keeps the program solvable at every state and the actuator limits exact, and we bound how far the input it returns can drift from the one the true models would give. On agile racing with a 6-D dynamic bicycle, the filter removes all 12 unsafe episodes from 200 held out layouts at an unchanged success rate and above 100 Hz on one CPU core, holds the input constraint on every episode of a benchmark where a jointly trained baseline holds it on 53.38%, and keeps that advantage under large errors in the plant friction.

cs.RO · cs.LG · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02625

Latent Diffusion-GAN: Adversarial Learning in the Autoencoded Latent Space

U-Chae Jun, Jaeeun Ko, Jiwoo Kang

原文摘要

Diffusion models are powerful generative frameworks for producing high-quality images by denoising latent variables from random noise. However, training with likelihood-based objectives, such as denoising score matching, can lead to locally over-smoothed high-frequency details, including fine textures and sharp edges, thereby limiting perceptual fidelity and structural detail. Adversarial training with GANs enhances sharpness but typically requires additional discriminator networks, increasing computational costs and destabilizing training. To this end, we propose Latent Diffusion Generative Adversarial Networks (LD-GAN), a novel framework that seamlessly integrates adversarial learning into diffusion models without modifying their original pipeline. LD-GAN leverages the pretrained variational autoencoder (VAE) in latent diffusion models as an energy-based discriminator, enabling adversarial training without extra parameters and preserving the structured latent priors learned from large datasets. We also introduce a structural consistency energy that aligns encoder and decoder feature representations, thereby enhancing perceptual quality and compatibility with the pretrained latent space. Extensive experiments demonstrate that LD-GAN significantly improves sample fidelity, perceptual sharpness, and diversity over state-of-the-art baseline methods across various generation tasks while ensuring efficient training dynamics.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02624

Widening Harm Evaluation Coverage with a Judge that Reads the World

Prabhpreet Singh Setia, Bhavesh Neekhra, Debayan Gupta

原文摘要

An agent acting in a stateful environment can leave evidence of harm in two places: the record of what it did and said, or the world it changed. Safety evaluations for discovering harm examine one or the other in limited ways, using either an LLM judge over the trajectory or deterministic checks over the final state. Although LLM-based verifiers that inspect the environment exist, they have been used to verify task completion, not to detect and confirm harm in a safety setting. We address this gap with an LLM that has read-only access to the environment state and is given a harm taxonomy. We evaluate this approach in three environments: two from DecodingTrust-Arena and one from OSWorld. We hold the judge model and harm taxonomy fixed, varying only what the judge observes: the trajectory, read-only tools for inspecting the environment, or both channels together. In the two DecodingTrust-Arena environments, combining the trajectory and state-only judges recalls 94 percent of confirmed harms, compared with 53 percent for the trajectory judge alone. On OS-Harm, the union recalls 77 percent, compared with 67 percent. A single judge given both channels does not perform better and produces more false negatives than the state-only judge. We also record disagreements and find that the two channels miss different episodes. These results suggest that combining verdicts from two channel-specific judges is a robust and flexible approach to detecting and verifying harm in agentic settings.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02623

Ask More, Hear Less: How a Review Prompt Changes What Online Ratings Show

Zihan Chen, Lei Nico Zheng, Di Zhu

原文摘要

Platforms ask customers for online reviews to reduce self-selection, but theory conflicts on whether review solicitation makes online ratings more or less informative. Selection theory implies that asking helps, voice theory that it dilutes the news, and research on rating inflation that compression destroys information. We reconcile these views with signal detection theory, which separates what a rating knows (its sensitivity) from what it shows (its criterion). We test the framework on Steam, a leading PC video game store, whose client began in October 2019 to prompt players for a review after about three hours of play. Using a difference-in-discontinuities design at that inferred threshold in 21.6 million Steam reviews of 315 games, we find that review counts just above the threshold become 4.25 times those just below, and the jump in the positive share grows by 6.6 percentage points. Agreement with critics changes little near launch and erodes later. The rating's movement changes far more: pass-through from reviews written in the first two hours falls from 0.73 to 0.18 just above the threshold, against 0.81 to 0.64 just below, and a fixed rating tier responds to 12% of downturns in those reviews above the threshold, down from 42%, against 41% and 47% below it. What the stream knows changes less. At equal review counts its sensitivity falls more above the threshold, because added reviews respond about half as much and scores near the ceiling are noisier per review, but at natural counts added volume offsets this loss, leaving a change of -0.03 relative to the stream below (95% CI -0.13 to 0.06). Failures that developers acknowledge, which run through complaints, still move the score after launch. Review prompts therefore mainly change how a rating must be read, and platforms that solicit reviews should recalibrate their rating labels.

cs.HC · cs.CY · econ.GN

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.24044

MERIS: Adaptive Real-Time Scheduling of Refined Inference DAGs in Dynamic Robotic Environments

Zexin Li, Tao Ren, Johnathan Liu, Xiaoxi He, Cong Liu

原文摘要

Robots deployed in dynamic environments execute changing directed-acyclic-graph (DAG) workloads under limited compute and timing budgets. This article presents MERIS, a framework for model scheduling under real-time constraints that combines proportional intermediate deadlines, MIMONet-aware graph refinement, and event-driven synchronization for multi-task inference on resource-constrained robotic platforms. We distinguish three representations: the application DAG, each MIMONet's internal encoder/decoder graph, and the refined scheduling DAG, and make the scope of the timing argument explicit. If the profiled execution bounds, release assumptions, precedence barriers, and per-level capacity premises hold, satisfaction of the assigned sub-deadlines composes to the end-to-end deadline. For non-partitionable tasks, we define a profile-bounded admission rule; before interpreting that rule as empirical timing evidence, we require direct per-task completion checks. We report run-level QoE alongside the offered-node deadline missing rate, making explicit that run completion and successful service of every node are different outcomes. We propose a comprehensive minibenchmark for evaluating the performance of schedulers under various DNN-driven autonomous driving scenarios. Extensive experiments on Nano Orin, AGX Orin, and M3 Max demonstrate the effectiveness of our proposed method.

cs.RO · cs.SE · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02620

Adaptive Localization of Knowledge Negation for Continual LLM Unlearning

Abudukelimu Wuerkaixi, Qizhou Wang, Sen Cui, Wutong Xu, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang

原文摘要

With the growing deployment of large language models (LLMs) across diverse domains, concerns regarding their safety have grown substantially. LLM unlearning has emerged as a pivotal approach to removing harmful or unlawful content while maintaining utility. Despite increasing interest, the challenges of continual unlearning, which is common in real-world scenarios, remain underexplored. Successive unlearning tasks often lead to intensified utility degradation. To effectively unlearn targeted knowledge while preserving LLM utility, it is essential to minimize changes in model parameters by selectively updating those linked to the target knowledge, thereby ensuring other knowledge remains unaffected. Building on the task vector framework, we propose a new method named ALKN (Adaptive Localization of Knowledge Negation), which uses dynamic masking to sparsify training gradients and adaptively adjusts unlearning intensity based on inter-task relationships. Comprehensive experiments across three well-established LLM unlearning datasets demonstrate that our approach consistently outperforms baseline methods in both unlearning effectiveness and utility retention under continual unlearning settings.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.03280

How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference

Toru Lin, Shuying Deng, Zhao-Heng Yin, Pieter Abbeel, Jitendra Malik

原文摘要

Many essential manipulation tasks - such as food preparation, surgery, and craftsmanship - remain difficult for autonomous robots. These tasks are characterized not only by contact-rich, force-sensitive dynamics, but also by their "implicit" success criteria: unlike pick-and-place, task quality in these domains is continuous and subjective (e.g. how well a potato is peeled), making quantitative evaluation and reward design difficult. We present a learning framework for such tasks, using peeling with a knife as a representative example. Our approach follows a two-stage pipeline: first, we learn a robust initial policy via force-aware data collection and imitation learning, enabling generalization across object variations; second, we refine the policy through preference-based finetuning using a learned reward model that combines quantitative task metrics with qualitative human feedback, aligning policy behavior with human-evaluated task quality. Using only 50~200 peeling trajectories, our system achieves 100% same-produce success on cucumbers, apples, and potatoes, with performance improving by up to 40% through preference-based finetuning. Remarkably, policies trained on one produce category also transfer zero-shot to selected held-out produce categories, achieving 50% success on zucchini, 90% on pear, and 80% on daikon radish.

cs.RO · cs.AI · cs.CV · cs.LG · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02618

USDB: Scaling Storage for Next-Generation Recommender Systems at ByteDance

Xiaoran Zhu, Jieming Cai, Weiping Yu, Xi Chen, Shu Cai, Jiahao Li, Yifan Xiao, Yan Deng 等 14 位作者

原文摘要

Recommendation models consume increasingly long, attribute-rich user histories, making small continuous updates and repeated large reads a central storage asymmetry. We present USDB, ByteDance's unified sequence storage practice for streaming and batch workloads. CascadeLSM time-based compaction stages materialize application-specific histories; differentiated replicas share persistent files; partial dumps reconstruct inference inputs from captured recent state and versioned history; and a hybrid columnar format accelerates projected reads. These designs reuse maintenance results across ingestion, inference, and training. Production migration records show 85.8% lower mean write latency, while normalized deployment accounting shows 42.1% lower infrastructure cost. Controlled comparisons with an Optimized RocksDB sequence baseline show faster long-history reads across projections and client loads. Aggregation pushdown sustains 8.5x the throughput of ordinary reads under a constrained receive budget. Production reconstruction and direct-file training results demonstrate how shared sequence maintenance supports fresh serving and repeated historical access.

cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02616

Herding or Racing? Decomposing Cross-Account Order-Flow Persistence in On-Chain Markets

Yichong Bai, Dingyue Kan

原文摘要

Most of the autocorrelation in trade signs on decentralized exchanges comes from pairs of trades sent by different wallets. For Uniswap v3, this has been read as herding dominating order splitting -- the reverse of what equity markets show. We show that this herding share mixes two quantities: the probability $p$ that consecutive orders come from the same account, which is small when thousands of wallets are trading, and the ratio $κ$ of cross-account to same-account sign correlation. For any $p$, $κ$ equals one under the public-signal benchmark of Tóth et al. and zero under independent order splitting. On Uniswap v3 USDC-WETH (one week each in 2023 and 2024), the herding share is 0.65-0.86, but $κ$ is only 0.05-0.10. On a fully on-chain EVM limit order book, the herding share is similar (0.61-0.87), but $κ$ is 0.72-0.76; among Uniswap swaps above USD 100k, it is 0.34-0.52. Where $κ$ is high, cross-account co-movement is mostly a race for the price on a centralized exchange. It is concentrated in orders a few blocks apart, and a cross-fitted gradient-boosted filter that sees only Binance data removes about three quarters or more of it. This race flow accounts for most of the losses of liquidity providers. We recommend reporting $κ$ alongside the herding share and modeling on-chain order flow as splitting traders plus arbitrageurs who respond to the centralized-exchange price.

cs.CE · q-fin.CP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02613

MAPO: Mixture of Experts with Alternating Policy Optimization for Universal Humanoid Motion Tracking

Seonghyeon Lim, Hyeonwoo Lee, Mincheol Kim, Kimin Lee, Hyun Myung

原文摘要

Universal humanoid motion tracking is essential for enabling humanoid robots to perform diverse tasks in human environments. To capture the wide diversity of human motions, mixture-of-experts (MoE) policies have been widely adopted, leveraging dynamic routing to enhance policy expressiveness. However, we identify that divergence between old and updated policies can be misestimated during policy-gradient updates of MoE policies, due to their dynamic routing. Because most policy-gradient algorithms regulate policy updates based on the divergence, this misestimation can lead to either overly conservative or excessively large updates, which can yield a suboptimal policy. In this paper, we propose Mixture of Experts with Alternating Policy Optimization (MAPO), which alternately optimizes the router and the experts to avoid the divergence misestimation. MAPO consistently tracks 182 motions that PPO with MoE fails to track, whereas PPO with MoE tracks only one motion that MAPO fails to track. MAPO also attains the highest tracking success rate among the compared methods and the lowest tracking error across most error metrics, demonstrating its superior performance. Videos can be found on the project website: https://mapo-corl2026.github.io/MAPO/

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.05264

Task-Aware Joint Pruning and Distillation for Efficient Audio Deepfake Detection

Miao He, Peng Cheng, Zhongjie Ba, Qing Wen, Li Lu, Xin Yang, Kui Ren

原文摘要

Advances in speech synthesis have made deepfake speeches increasingly convincing, posing growing threats to security. While self-supervised learning (SSL) based detectors achieve state-of-the-art performance, their computational demands (typically 300M+ parameters) prevent deployment on resource-constrained devices. Existing compression methods, designed mainly for content-centric tasks, struggle to maintain competitive performance when directly adapted to deepfake detection. We propose a Task-Aware Joint Pruning and Distillation framework that combines cross-domain knowledge distillation with movement-guided structured pruning to transfer forgery-discriminative knowledge and preserve critical structures under aggressive compression. Our framework reduces the model to 31.9M parameters with 6.3$\times$ FLOPs reduction, with an average performance drop of only 1.30\% across multiple datasets compared to the uncompressed baseline, demonstrating strong potential for on-device deployment.

cs.SD · cs.AI

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2605.19398

Rebalancing Reference Frame Dominance to Improve Motion in Image-to-Video Models

Wooseok Jeon, Seungho Park, Seunghyun Shin, Sangeyl Lee, Hyeonho Jeong, Hae-Gon Jeon

原文摘要

Image-to-video models often generate videos that remain overly static, compared to text-to-video models. While prior approaches mitigate this issue by weakening or modifying the image-conditioning signal, they often require additional training or sacrifice fidelity to the reference image. In this work, we identify reference-frame dominance as a key mechanism behind motion suppression. We observe that non-reference frames in I2V models allocate excessive self-attention to reference-frame key tokens, causing reference information to be over-propagated across time and suppressing inter-frame dynamics. Based on this finding, we propose DyMoS (Dynamic Motion Slider), a training-free and model-agnostic method that rebalances the attention pathway from generated frames to the reference frame during initial denoising steps. DyMoS leaves both the input image and model weights unchanged and introduces a single scalar parameter for continuous control over motion strength. Experiments across multiple state-of-the-art I2V backbones demonstrate that DyMoS consistently improves motion dynamics while maintaining visual quality and fidelity to the reference image.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2602.02220

LangMap: A Human-Verified Benchmark for Hierarchical Open-Vocabulary Goal Navigation

Bo Miao, Weijia Liu, Jun Luo, Lachlan Shinnick, Jian Liu, Thomas Hamilton-Smith, Yuhe Yang, Zijie Wu 等 11 位作者

原文摘要

Language-conditioned goal navigation requires embodied agents to locate user-specified targets without step-by-step guidance. However, existing benchmarks largely focus on category-level goals or rely on instance descriptions generated by vision-language models, which often contain ambiguities and semantic errors, limiting systematic and reliable evaluation. We introduce HieraNav, an open-vocabulary navigation task with goals at four hierarchical semantic levels: scene, room, region, and instance. We present Language as a Map (LangMap), the first goal navigation benchmark to enrich real-world indoor 3D scans with human-verified semantic annotations supporting tasks across all four goal levels. Built on HM3D using a contrastive annotation protocol that compares same-scene regions and instances, LangMap provides region labels and discriminative region and instance descriptions covering 414 object categories and contains over 18K tasks. Each target has concise and detailed descriptions, enabling evaluation across instruction styles. Automated and human evaluations validate our annotation quality: our descriptions improve text-to-view matching accuracy over GOAT-Bench's by 23 points on all shared annotated instances, and an independent human audit yields 92.5% unique-and-correct matches. We also propose PlaNaVid, an RGB-only baseline that combines Bounded Diverse Memory with high-level planning to prime a reactive policy for multi-goal navigation, achieving top-tier success rates without depth, 3D scene representations, or object masks. Further analyses reveal that exploration and hierarchical disambiguation failures become more prominent at finer goal levels, while long-tail categories, small objects, distant targets, timely stopping, and multi-goal completion remain challenging. Benchmark and code: https://bo-miao.github.io/LangMap

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02612

Scalaris: A Scalable Enterprise Memory Architecture for Conversational AI Assistants

Devarsh Gandhi, Sarthak Pawar, Hemprasad Badgujar, Vaibhav Rathore, Siddharth Sureka, Neha Bhargava

原文摘要

Enterprise conversational systems require memory to personalize interactions, retain information across sessions, and maintain conversational coherence. At enterprise scale, however, memory must not only retrieve relevant information accurately but also provide it efficiently to the answer-generation model. Existing memory systems face a trade-off between retrieval accuracy and context efficiency. Retrieving more context can improve recall but increases inference cost and latency. We present Scalaris, a scalable enterprise memory architecture that addresses this trade-off through a unified long-term and short-term memory design. We propose that user interactions and assistant responses possess distinct characteristics and roles in a conversation: user interactions capture evolving knowledge about the user and provide value across sessions, whereas assistant responses must be stored efficiently so that only information relevant to the current query is retrieved. We therefore treat them separately and support each with specialized representations and ingestion/retrieval pipelines. Scalaris reaches 92.2 % accuracy on LongMemEval-S and 90.77 % on LoCoMo using Qwen3-32B, state-of-the-art results on both benchmarks, while supplying the answer-generation model with approximately 10 times fewer retrieved context tokens than Hindsight, a leading memory system for conversational AI. When integrated with our proprietary closed-source AI assistants, Scalaris improved overall response quality by 36 %. Together, these results establish Scalaris as a cost-effective framework for building scalable, personalized conversational AI systems.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02611

Where Does MCP Security End? A Three-Tier Mitigation Mapping Framework for the OWASP MCP Top 10

Christopher Krakauer, Andreas Schaad

原文摘要

The Model Context Protocol (MCP) has become a leading standard for connecting Large Language Models (LLMs) to external tools, software, and data sources. MCP architecture favors ease of integration over security, blending the data and control planes into a single natural language channel. This introduces confused deputy and prompt injection vulnerabilities that cannot be completely mitigated in the software development lifecycle (SDLC) of an MCP server. Structured security guidance for server developers has been outpaced by this adoption trend, leaving them without a clear way to identify where vulnerabilities can be addressed at the server layer and where architectural constraints create hard limits. This paper presents a framework based on OWASP MCP Top 10 mapped to three architectural tiers, developed using Design Science Research (DSR). Vulnerabilities are classified as server-primary, gateway-primary, or protocol-primary applying a cascading decision tree following four questions, resulting in reproducible tier assignments and confidence levels independent of who applies it. The framework was validated against an enterprise-grade Managed File Transfer (MFT) MCP server built in Go. Validation results showed that high-confidence server-tier controls worked as predicted, and low-confidence protocol-tier controls failed for architectural reasons beyond server implementation. The architectural ceiling encountered during practical validation is the main contribution and shows how secure-by-design development can reduce the MCP attack surface but not completely mitigate it.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.04822

Minimax Rate for Diffusion-Guided Local Intrinsic Dimension Estimation based on Fokker-Planck Equation

Jaehee Seo, Wontae Jeong, Jisu Kim

原文摘要

Diffusion-guided methods have recently emerged as effective tools for probing the intrinsic geometry of high-dimensional data. Understanding their accuracy requires quantifying both (i) finite-scale approximation error relative to the true local intrinsic dimension (LID) and (ii) estimation error from finite samples. We address these two questions for the FLIPD field (Kamkari et al., 2024; arXiv:2406.03537), defined at the population level through the logarithmic scale derivative of a Gaussian-smoothed density and admitting a Fokker--Planck representation. For $α$-smooth densities on a regular $d$-dimensional manifold, we establish a uniform $O(σ^2)$ approximation bound relative to the manifold dimension $d$. Under expected integrated squared loss, we prove a minimax lower bound of order $(nσ^d)^{-1}$ for estimating the FLIPD field from $n$ observations when $n^{-1/(2α+d)}\leσ\leσ_0$. A plug-in FLIPD estimator based on the empirical denoising score matching (DSM) solution attains a matching upper bound. Thus the minimax rate is of order $(nσ^d)^{-1}$ and it becomes $n^{-2α/(2α+d)}$ at its smallest scale.

stat.ML · cs.LG · math.ST

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.39884

KARMA: Training-Free Knowledge-Atomic Harness Evolution for Industrial Advertising Agents

Xinhua Miao, Yuanzhen Rong, Weiru Zhang, Xiaoyi Zeng

原文摘要

Industrial advertising agents must continually adapt their decision knowledge to changing product offerings, traffic patterns, and operating policies. We present KARMA (Knowledge-Atomic, Regression-Gated Modular Adaptation), a training-free framework that evolves six typed knowledge modules while keeping model weights and executable harness code frozen. During our evolution, a deterministic Trace Verifier maps observed errors to candidate modules, and a Critic compares successful and failed trajectories to guide a tool-constrained Evolver. The Evolver applies atomic knowledge edits, which a fixed validation gate retains only when their measured gain reaches a threshold; otherwise, the previous knowledge state is restored. We evaluate KARMA with three LLM families on cases sampled from a corpus of approximately half a million real-world operation logs across six e-commerce domains. Across 18 domain--model settings, KARMA raises the average hold-out score from 30.19 to 39.15 (+8.96 points, +29.7%). Ablations show that removing either successful or failed trajectories more than halves the average hold-out gain. Evolved knowledge also transfers across domains without further adaptation, yielding an average hold-out gain of 8.54 points across all source--target pairs. By linking retained patches to their diagnostic evidence and validation outcomes, KARMA supports inspectable, reversible knowledge adaptation for agents in Web marketplaces.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2508.04903

RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory

Jun Liu, Zhenglun Kong, Changdi Yang, Fan Yang, Tianqi Li, Peiyan Dong, Joannah Nanjekye, Hao Tang 等 15 位作者

原文摘要

Large language model (LLM) agents are increasingly organized into multi-agent systems for complex reasoning and decision-making tasks. However, scaling such systems can substantially increase inference cost, especially when multiple agents repeatedly process overlapping or role-irrelevant context. Many existing multi-agent frameworks focus on orchestration and role specialization, but still rely on full-context sharing or static context routing, leading to redundant token consumption, increased runtime, and inefficient memory utilization. We present \emph{Role-Conditioned Context Routing (RCR)}, an execution-time layer that allocates role-relevant shared-memory items to agents under token budgets. Given a structured shared memory and a set of role-specialized agents, RCR selectively routes task-relevant memory items to each agent according to its role, optional task-stage metadata, and token budget. RCR integrates role-conditioned relevance scoring, token-budgeted memory selection, and iterative memory updates to reduce redundant context exposure during multi-agent execution. Experiments on multi-hop QA, embodied reasoning (ALFWorld), and web-based decision-making (WebShop) show that RCR reduces token usage by 35\% on average and runtime by 38\% on average relative to Full-Context, while maintaining or improving task performance.

cs.CL · cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02609

Whitened Advantage Summation Policy Optimization: Correlation-Aware Aggregation for Multi-Reward Reinforcement Learning

Olaitan Olaleye, Jayson Meribe, Temidayo Olaleye

原文摘要

Reinforcement learning for language models often combines several rewards by normalizing each one and adding them with fixed weights. When the rewards are correlated, the variation they share is counted once for every reward that carries it. In terms of the ZCA-decorrelated components of the reward signal, the weighted sum applies weights $Σ^{1/2} w$ rather than the chosen weights $w$, where $Σ$ is the reward correlation matrix. We study WAS-PO, which whitens the normalized advantages with $Σ^{-1/2}$ before adding them, so that the chosen weights apply to those components. We derive the scalarized objective whose gradient it follows when normalization statistics are held fixed, show that its weights on the raw rewards can be negative, give an exact two-reward condition under which the lighter-weighted reward is reversed, and give a loose bound on the error from estimating $Σ$ during training. On tool calling with Qwen2.5-1.5B-Instruct, WAS-PO reached a higher mean training reward than GDPO in all seven seeds with three rewards (3.8% higher) and all three seeds with four (3.9% higher). WAS-PO also replaces GDPO's per-prompt normalization with batch-level normalization, and we did not separate the two changes with three or four rewards. The training-reward gains did not carry over to held-out accuracy, which was slightly lower than GDPO's with three rewards (not significant) and 0.03 lower in absolute terms with four (two seeds evaluated). With two rewards, WAS-PO trailed GDPO in training reward, and a three-seed control weakly suggests that the gap comes from batch-level normalization rather than whitening. In a math setting where the two rewards were nearly identical by construction, WAS-PO drove down the lighter-weighted reward, as the sign condition predicts.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02605

StateFence: Task-State Protection Against Evidence-Chain Contamination in Blue-Team LLM Agents

Haowen Dai, Alejandro Guerra-Manzanares

原文摘要

Indirect prompt injection creates a distinctive risk for blue-team Large Language Model (LLM) agents in Security Operations Centers (SOCs). Malicious instructions can be embedded in telemetry, tickets, retrieval results, or memory and then propagate through alert triage, incident summarization, response recommendation, and Indicator of Compromise (IOC) extraction as locally plausible intermediate decisions. We characterize this failure mode as evidence-chain contamination of protected task state. The main challenge is task dependent: a suspicious string may be malicious control text for summarization but essential evidence for IOC extraction. To address this challenge, we propose StateFence, a task-aware evaluation and defense framework that treats severity labels, incident narratives, response policies, and IOC sets as protected states. StateFence maps indirect prompt-injection attacks into explicit, stealth, multi-hop, and persistent SOC evidence attacks, records observable propagation traces, and routes defenses according to the attacked state. Across controlled SOC cases and public-SOC validation cases, StateFence lowers attack success and reduces failures that appear only after evidence is poisoned, while preserving task utility and avoiding the indicator loss caused by task-mismatched filtering. Our findings indicate that robust blue-team LLM agents require mechanisms that preserve task-critical state under untrusted evidence.

cs.CR · cs.AI

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arXiv ↗PDF ↗